Prattoy Majumder
ABSTRACT
Crimes using various types of vehicles are increasing day by day. Crime rates in borders are remarkably high such as smuggling drugs, people, animals along with many other things. So, it is very essential to make our borders and roads safer. In this paper, we suggest a system to analyze criminal behavior for questionable vehicle detection using automobile license plate recognition (ALPR) system with face recognition system on moving vehicles and matching with blacklisted people. By finding out people's identities we can verify much easily if the people in any vehicle are involved with any types of crimes. But, in developing countries, it is very hard to build smart questionable vehicle detection system with very little ground work. There are also less data to work with. The result of this study shows that, the ALPR system with face recognition system on moving vehicles will improve the efficiency of questionable vehicle detection system.
- A. Altman and M. Tennenholtz. 2005. Ranking Systems: The Pagerank Axioms. Proceedings of the 6th ACM Conference on Electronic Commerce (2005), 1--8. https://doi.org/10.1145/1064009.1064010Google Scholar
Digital Library
- Z. Chen, N. Pears, M. Freeman, and J. Austin. 2014. A gaussian mixturemodel and support vector machine approach to vehicle type and colour classification. IET Intelligent Transport Systems 8, 2 (2014), 135--144.Google ScholarCross Ref
- Changxing Ding, Chang Xu, and Dacheng Tao. 2015. Multi-Task Pose-Invariant Face Recognition. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society (2015). https://doi.org/10.1109/TIP.2015.2390959Google ScholarDigital Library
- Q. Ding, X. Li, M. Jiang, and X. Zhou. 2010. Reputation Management in Vehicular Ad Hoc Networks. Multimedia Technology (ICMT) (Oct. 2010), 1--5.Google Scholar
- P. L. Hsieh, Y. M. Liang, and H. Y. M. Liao. 2010. Recognition of blurred license plate images. 2010 IEEE International Workshop on Information Forensics and Security (Dec. 2010), 1--6.Google ScholarCross Ref
- C. Hu, X. Bai, L. Qi, X. Wang, G. Xue, and L. Mei. 2015. Learning Discriminative Pattern for Real-Time Car Brand Recognition. IEEE Transactions on Intelligent Transportation Systems 16, 6 (Dec. 2015), 3170--3181.Google ScholarDigital Library
- A. H. S. Lai and N. H. C. Yung. 2000. Vehicle-Type Identification Through Automated Virtual Loop Assignment and Block-Based Direction-Biased Motion Estimation. IEEE Transactions on Intelligent Transportation Systems (June 2000).Google ScholarDigital Library
- L. Lazzari, M. Mari, and A. Poggi. 2005. A collaborative and multi-agent system for E-mail filtering and classification. Collaborative Computing: Networking, Applications and Worksharing (2005), 8.Google Scholar
- Zhifeng Li, Dihong Gong, Xiaonan Li, and Dacheng Tao. 2015. Learning Compact Feature Descriptor and Adaptive Matching Framework for Face Recognition. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society (2015). https://doi.org/10.1109/TIP.2015.2426413Google ScholarDigital Library
- L. Page, S. Brin, R. Motwani, and T. Winograd. 1998. The Pagerank Citation Ranking: Bringing Order to the Web. Proceedings of the 7th International World Wide Web Conference (1998), 161--172. citeseer.nj.nec.com/page98pagerank.htmlGoogle Scholar
- S. Park, B. Aslam, and C. Zou. 2011. Long-term reputation system for vehicular networking based on vehicle's daily commute routine. Consumer Communications and Networking Conference (CCNC) (Jan. 2011).Google Scholar
- P. Resnick and R. Zeckhauser. 2002. Trust Among Strangers in Internet Transactions: Empirical Analysis of eBay's Reputation System. The Economics of the Internet and E-Commerce 11 (2002), 127--157.Google ScholarCross Ref
- B. Taylor. 2006. Sender Reputation in a Large Webmail Service. CEAS (2006).Google Scholar
- U. Thongsatapornwatana and C. Chuenmanus. 2015. Suspect Vehicle Detection Using Vehicle Reputation with Association Analysis Concept. Image Processing (ICIP) (2015).Google Scholar
- U. Thongsatapornwatana, W. Lilakiatsakun, A. Kawbunjun, and T. Boongoen. 2017. Analysis of Criminal Behaviors for Suspect Vehicle Detection. The Twelfth International Conference on Digital Information Management (ICDIM 2017) (2017).Google Scholar
- Jing Wang, Qiwen Zha, Yubo Yang, Yang Liu, Bo Yang, Dengbiao Tu, and Guangda Su. 2015. Facial Stereo Processing by Pyramidal Block Matching. (08 2015), 252--260. https://doi.org/10.1007/978-3-319-21963-9_23Google ScholarCross Ref
- Niv Zehngut, Felix Juefei-Xu, Rishabh Bardia, Dipan Pal, Chandrasekhar Bhagavatula, and Marios Savvides. 2015. Investigating the feasibility of image-based nose biometrics. (09 2015), 522--526. https://doi.org/10.1109/ICIP.2015.7350853Google ScholarDigital Library
- Zhuofeng Zhao, Weilong Ding, Jianwu Wang, and Yanbo Han. 2015. A Hybrid Processing System for Large-Scale Traffic Sensor Data. IEEE Access 3 (2015).Google ScholarCross Ref
Index Terms
- Criminal Behavior Analysis for Questionable Vehicle Detection
Recommendations
Training method for vehicle detection
ICCIP '16: Proceedings of the 2nd International Conference on Communication and Information ProcessingRecently, vehicle detection methods have been popularly used in the field of intelligent vehicles. The performance and processing time of vehicle detection is very important because it is associated with the life of a driver. However, all vehicle ...
Nighttime vehicle light detection on a moving vehicle using image segmentation and analysis techniques
This study proposes a vehicle detection system for identifying the vehicles by locating their headlights and rear-lights in the nighttime road environment. The proposed system comprises of two stages for detecting the vehicles in front of the camera-...
On-Road Vehicle Detection: A Review
Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a ...
Comments